Fast Linear Model Predictive Control Via Custom Integrated Circuit Architecture

Wills, Adrian; Knagge, G.; Ninness, Brett

doi:10.1109/tcst.2010.2096224

Cited by 64 publications

(54 citation statements)

References 26 publications

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“…A further contribution, is the verification of this control design by application to an experimental apparatus. This complements previous work by the authors where the use of an alternative active-set method for solving the optimisation problem was examined [26]. Examining the relative benefits and tradeoffs involved with these two approaches to MPC implementation is a topic of current interest [11], and it is intended that the work here in combination with [26] contributes to this area.…”

Section: Introductionmentioning

confidence: 53%

“…The two dominant standard approaches in this context are so-called "active-set" and "interiorpoint" methods. The authors have considered the active-set method in related work [10,25,26], but in this paper we employ an interior-point technique (see e.g. [31]) to solve the quadratic program in (3).…”

Section: Problem Formulationmentioning

confidence: 99%

“…This is in contrast to an active-set solution where a sequence of linear systems are solved and whose corresponding size is related to the number of active constraints. The latter feature of an active-set method complicates the FPGA implementation since a range of matrix and vector sizes must be internally tracked [26]. The interior-point method used here employs a primal logarithmic-barrier approach since it is particularly well suited to the MPC situation considered here.…”

Section: Problem Formulationmentioning

confidence: 99%

“…By way of motivation, relative to a standard architecture microprocessor solution an FPGA-based custom architecture solution (such as presented here and in [26]) has two advantages. It can simply and directly be "dropped in" as a computing core in a larger embedded systems design.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementation of linear model predictive control using a field-programmable gate array

Mills

Wills

Weller

et al. 2012

IET Control Theory Appl.

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The paper investigates the design of a field programmable gate array based custom computer architecture solution for implementing model predictive control. The solution employs a primal logarithmic-barrier interior-point algorithm in order to handle actuator constraints. The solution also incorporates practical aspects of a control algorithm including state observation and data sampling. The resulting circuit is profiled by application to a disturbance rejection control problem of a 14'th order lightly damped flexible beam structure with actuator constraints. This is achieved at 2 kHz sampling frequency and with 16-sample prediction horizon.

show abstract

Section: Introductionmentioning

confidence: 53%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of linear model predictive control using a field-programmable gate array

Mills

Wills

Weller

et al. 2012

IET Control Theory Appl.

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“…Algorithms that target embedded platforms are available in FiOrdOs [8], FORCES [5], CVXGEN [9], qpOASES [3], and MPT3 [10], just to name a few. Earlier work on the implementation aspects of MPC on embedded hardware are reported in [11], [12], [13], [14], [15], [7], where a common goal is the efficient use of resource constrained embedded hardware. Steps towards real-time guarantees or effects of limited computation time on MPC stability and feasibility have also been made in recent research work [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Embedded Model Predictive Control on a PLC using a primal-dual first-order method for a subsea separation process

Kufoalor

Richter

Imsland

et al. 2014

22nd Mediterranean Conference on Control and Automation

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Abstract-The results of a PLC implementation of embedded Model Predictive Control (MPC) for an industrial problem are presented in this paper. The embedded MPC developed is based on the linear MPC module in SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control), and it combines custom ANSI C code generation with problem size reduction methods, embedded real-time considerations, and a primal-dual first-order method that provides a fast and light QP solver obtained from the FiOrdOs code generator toolbox. Since the primal-dual first-order method proposed in this paper is new in the control community, an extensive comparison study with other state-of-the-art first-order methods is conducted to underline its potential. The embedded MPC was implemented on the ABB AC500 PLC, and its performance was tested using hardware-in-the-loop simulation of Statoil's newly patented subsea compact separation process. A warm-start variant of the proposed first-order method outperforms a tailored interiorpoint method by a factor of 4 while occupying 40% less memory.

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Embedded MPC Controller Based on Interior‐Point Method with Convergence Depth Control

Ding

Zhao

et al. 2016

Asian Journal of Control

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To allow the implementation of model predictive control on the chip, we first propose a primal-dual interior point method with convergence depth control to solve the quadratic programming problem of model predictive control. Compared with algorithms based on traditional termination criterion, the proposed method can significantly reduce the computation cost while obtaining an approximate solution of the quadratic programming problem with acceptable optimality and precision. Thereafter, an embedded model predictive controller based on the quadratic programming solver is designed and implemented on a digital signal processor chip and a prototype system is built on a TMDSEVM6678LE digital signal processor chip. The controller is verified on two models by using the hardware in loop frame to mimic real applications. The comparison shows that the whole design is competitive in real-time applications. The typical computation time for quadratic programming problems with 5 decision variables and 110 constraints can be reduced to less than 2 ms on an embedded platform.

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Fast Linear Model Predictive Control Via Custom Integrated Circuit Architecture

Cited by 64 publications

References 26 publications

Implementation of linear model predictive control using a field-programmable gate array

Implementation of linear model predictive control using a field-programmable gate array

Embedded Model Predictive Control on a PLC using a primal-dual first-order method for a subsea separation process

Embedded MPC Controller Based on Interior‐Point Method with Convergence Depth Control

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